This chapter explores the integration of Computer Vision (CV) techniques into Asset and Facility Management (FM). It begins by defining FM as a discipline that integrates people, place and process to improve operational efficiency and quality of life in the built environment, as defined in EN ISO 41011. The chapter highlights the critical role of CV in automating key FM functions, such as condition inspection, monitoring, and digital twin generation, to improve decision making and efficiency. It reviews advanced 3D reconstruction and deep learning approaches, highlighting their ability to address the challenges of capturing and modelling the as-built condition of existing assets. A key theme is the use of synthetic data to overcome data scarcity in FM-specific applications. Using 3D BIM models and graphics engines such as Blender, the chapter demonstrates how synthetically generated datasets can enhance object recognition models, enabling the identification of FM-related components such as HVAC systems and electrical fixtures. The study validates the proposed pipeline using YOLO-based object detection models, demonstrating significant performance improvements when combining synthetic and real data. This work highlights the potential of CV and synthetic data to transform FM practices, promoting improved asset management and operational efficiency.